IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v278y2020ics0306261920311545.html
   My bibliography  Save this article

A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption

Author

Listed:
  • Wu, Wenbo
  • Dong, Bing
  • Wang, Qi (Ryan)
  • Kong, Meng
  • Yan, Da
  • An, Jingjing
  • Liu, Yapan

Abstract

In the US, people spend more than 90% of their time in buildings, which contributes to more than 70% of overall electricity usage in the country. Occupant behavior is becoming a leading factor impacting energy consumption in buildings. Existing occupant-behavior studies are often limited to a single building and individual behavior, such as presence or interactions in confined spaces. Moreover, studies modeling occupant behavior at the building or community level are limited. With the development of the Internet of Things, mobile positioning data are available through social media and location-based service applications. The goal of this study is to analyze the impacts of more representative occupancy profiles, derived from high resolution urban scale mobile position data, on building energy consumption. . A pilot study was conducted on more than 900 buildings in downtown San Antonio, Texas, with billions of mobile positioning data. We then compared these profiles with the existing Department of Energy prototype models and quantified the differences using a statistical method. On average, the differences in occupancy rates between the ones derived from the empirical profile and the ones from the Department of Energy reference ranged from −30% to 70%. The realistic derived profiles are then simulated in the CityBES. The results show that the predicted cooling energy demand is reduced by up to 40% while the heating energy demand is reduced by up to 60%. This study, therefore, advances knowledge of urban planning as well as urban-scale energy modeling and optimization.

Suggested Citation

  • Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920311545
    DOI: 10.1016/j.apenergy.2020.115656
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920311545
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115656?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qi Wang & Nolan Edward Phillips & Mario L. Small & Robert J. Sampson, 2018. "Urban mobility and neighborhood isolation in America’s 50 largest cities," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(30), pages 7735-7740, July.
    2. Mohammadi, Neda & Taylor, John E., 2017. "Urban infrastructure-mobility energy flux," Energy, Elsevier, vol. 140(P1), pages 716-728.
    3. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    4. Luca Pappalardo & Filippo Simini & Salvatore Rinzivillo & Dino Pedreschi & Fosca Giannotti & Albert-László Barabási, 2015. "Returners and explorers dichotomy in human mobility," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    5. Alaia Sola & Cristina Corchero & Jaume Salom & Manel Sanmarti, 2018. "Simulation Tools to Build Urban-Scale Energy Models: A Review," Energies, MDPI, vol. 11(12), pages 1-24, November.
    6. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Sawaya, Geof & Chen, Yixing & Taylor-Lange, Sarah C., 2015. "Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance," Energy, Elsevier, vol. 90(P1), pages 738-747.
    7. Jungmin Kim & Juyong Park & Wonjae Lee, 2018. "Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    8. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    9. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. A P Riascos & José L Mateos, 2017. "Emergence of encounter networks due to human mobility," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-22, October.
    11. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    12. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    13. Edward Barbour & Carlos Cerezo Davila & Siddharth Gupta & Christoph Reinhart & Jasleen Kaur & Marta C. González, 2019. "Planning for sustainable cities by estimating building occupancy with mobile phones," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    14. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    15. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    2. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    3. Ehsan Kamel, 2022. "A Systematic Literature Review of Physics-Based Urban Building Energy Modeling (UBEM) Tools, Data Sources, and Challenges for Energy Conservation," Energies, MDPI, vol. 15(22), pages 1-24, November.
    4. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    5. Piselli, Cristina & Salvadori, Giacomo & Diciotti, Lorenzo & Fantozzi, Fabio & Pisello, Anna Laura, 2021. "Assessing users’ willingness-to-engagement towards Net Zero Energy communities in Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    6. Chong, Adrian & Augenbroe, Godfried & Yan, Da, 2021. "Occupancy data at different spatial resolutions: Building energy performance and model calibration," Applied Energy, Elsevier, vol. 286(C).
    7. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    2. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    4. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    5. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    6. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    7. Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
    8. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    9. Valeria Todeschi & Roberto Boghetti & Jérôme H. Kämpf & Guglielmina Mutani, 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    10. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    11. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    12. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    13. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
    14. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    15. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    16. Rachael Sherman & Hariharan Naganathan & Kristen Parrish, 2021. "Energy Savings Results from Small Commercial Building Retrofits in the US," Energies, MDPI, vol. 14(19), pages 1-16, September.
    17. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    18. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    19. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    20. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920311545. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.